In the pre-training stage, we use the Barlow Twins loss function to learn graph representations from crystals. This loss is based on the redundancy reduction principle proposed by neuroscientist H. Barlow [1]}, [2]} and was introduced to SSL by Zbontar et al.[3]}. We use the Barlow Twins loss function in CT because of its high performance and ease of implementation. The Barlow Twins loss function is applied to the cross-correlation matrix created from encoder-generated embeddings of the two different augmentations of the same crystalline system. The Barlow Twins loss function is represented by Equation REF ,
\(L_{BT} \overset{\Delta }{=} \sum _i(1-C_{ii})^2 + \lambda \sum _i\sum _{j\ne i} C_{ij}^2,\) 
